A single-pass type I membrane protein, mannose-specific L-type lectin, potentially involved in the adhesion and invasion of Cryptosporidium parvum.
Xiaotian ZhangSongying SunWenchao ZhaoLuyang WangGuanda LiangYuexin WangBaiyi CaiLongxian ZhangXiaoying LiSumei ZhangPublished in: Parasite (Paris, France) (2024)
Cryptosporidium is a globally distributed zoonotic protozoan parasite that can cause severe diarrhea in humans and animals. L-type lectins are carbohydrate-binding proteins involved in multiple pathways in animals and plants, including protein transportation, secretion, innate immunity, and the unfolded protein response signaling pathway. However, the biological function of the L-type lectins remains unknown in Cryptosporidium parvum. Here, we preliminarily characterized an L-type lectin in C. parvum (CpLTL) that contains a lectin-leg-like domain. Immunofluorescence assay confirmed that CpLTL is located on the wall of oocysts, the surface of the mid-anterior region of the sporozoite and the cytoplasm of merozoites. The involvement of CpLTL in parasite invasion is partly supported by experiments showing that an anti-CpLTL antibody could partially block the invasion of C. parvum sporozoites into host cells. Moreover, the recombinant CpLTL showed binding ability with mannose and the surface of host cells, and competitively inhibited the invasion of C. parvum. Two host cell proteins were identified by proteomics which should be prioritized for future validation of CpLTL-binding. Our data indicated that CpLTL is potentially involved in the adhesion and invasion of C. parvum.
Keyphrases
- cell migration
- induced apoptosis
- signaling pathway
- cell cycle arrest
- endoplasmic reticulum stress
- binding protein
- mass spectrometry
- pi k akt
- high throughput
- stem cells
- amino acid
- early onset
- machine learning
- cell therapy
- cell death
- single cell
- staphylococcus aureus
- transcription factor
- low density lipoprotein
- drug induced
- mesenchymal stem cells
- artificial intelligence
- cell adhesion
- neural network
- deep learning
- irritable bowel syndrome